Proteomics LIMMA Regression - James Johnson - v1-18-22

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#Load some useful libraries
library(limma)
library(DESeq2)
library(edgeR)
library(data.table)
library(tidyr)
library(magrittr)
library(tidyverse)
library(sjlabelled)
library(sjmisc)
library(ggplot2)
library(broom)
library(DataCombine)

#import data
countdf <- DropNA(read.csv(file = 'proteomics.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(countdf) <- NULL
meta <- DropNA(read.csv(file = 'proteomics_metadata.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(meta) <- NULL
anno <- DropNA(read.csv(file = 'proteomics_metadata_table.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(anno) <- NULL
full <- DropNA(read.csv(file = 'proteomics_full.csv'))

No Var specified. Dropping all NAs from the data frame.

0 rows dropped from the data frame because of missing values.

rownames(full) <- NULL

#clean-up tables
#prepare for model
#make consistent index/column/label/ordering between tables/matrices
names(countdf)[1] <- 'name'
#countdf[countdf==0] <- 0.0000001
#countdf[countdf<0] <- 0.0000001
names(countdf) <- sapply(str_remove_all(names(countdf),"X"),"[") # remove added X's from PCI names
count_matrix <- countdf[,2:ncol(countdf)]
count_matrix <- as.matrix(count_matrix)
#count_matrix[,1] <- NULL
names(full) <- sapply(str_remove_all(colnames(full),"X"),"[")
names(count_matrix) <- sapply(str_remove_all(colnames(count_matrix),"X"),"[")
meta$sex <- factor(meta$sex)
full$sex <- factor(full$sex)
meta$bowel <- factor(as.numeric(factor(meta$bowel, levels = c(1,2,3,4), labels = c(1,2,3,4))))
full$bowel <- factor(as.numeric(factor(full$bowel, levels = c(1,2,3,4), labels = c(1,2,3,4))))
meta <- within(meta, bowel <- relevel(bowel, ref = 3))
full <- within(full, bowel <- relevel(bowel, ref = 3))
count_matrix

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         01807510   01808734  01809016   01812156   01814069   01814335   01814396   01814606   01815158   01815820   01816988  01817283   01817289
        01819087  01819282   01819787   01820163  01820666   01820710  01821427  01821454   01821552  01822309   01822602   01822702  01823769
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         01849109   01849730   01849944  01850412   01851966   01852207  01852337   01852342   01852965   01853112   01853524   01853575  01853660
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         01859809  01859929  01859995   01860248  01860486   01860772  01861175  01861775   01862266   01863420   01863617   01864232   01864390
         01864964   01865441  01865446   01866098   01866327   01866472   01866618  01867779   01868438   01869208  01869613   01870183  01870642
         01870822  01871379   01871388   01872393   01872692   01873155  01873627   01874023   01874114  01874421  01875465   01875466  01875589
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         01901038   01901814   01902049   01902313   01902621  01903562   01903748   01905229   01905932   01906832  01906856   01907066   01907416
        01907575  01907913   01908830  01909024   01909532   01909625   01909807   01910034   01911124   01911172  01911364   01911370  01912726
         01912853   01913481   01913933  01914451   01915036   01915440   01916045  01916479   01916558   01917148   01917153  01917395  01918227
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         01924225   01925138   01925174   01925178   01925800   01926181   01926351  01926397   01926537   01926705  01927157  01927181   01927185
         01927208   01928201   01929506   01929517   01930116  01930547  01930731  01930960   01931778   01931849   01931898   01932120  01932384
        01932611  01932693   01933205   01933291   01934177   01934222  01935644   01935829  01935930   01936132   01937405   01937921   01938378
        01938606  01939058  01939189  01939555   01940575   01940618   01941086  01941134   01941857  01942853  01943201   01943712  01944942
         01945564   01946056   01946852   01947387  01947733   01948018   01948580   01948634   01949838   01950342   01952180  01952330   01953116
        01953398   01954001  01954492  01954615   01954804   01954947  01955210   01955250  01955427   01955611   01956344   01958214   01958339
         01959030   01959080   01959804  01959844   01959943   01960296  01960682   01960736   01960876  01962038   01962253   01962385  01962646
        01963140   01963243  01963556   01964190  01964339  01965364   01965921   01965975   01966606   01966838   01968285  01968623  01969364
        01969821  01970438   01971060  01971659   01971921  01972000   01972700   01974110  01974625   01974833   01975030   01975130   01975702
         01976404   01976567  01980181  01980311   01980444  01980931  01981511  01982074   01982352   01982907   01983318   01984058   01984497
         01984762   01984864   01984952   01985328   01985884   01986408  01987388  01987914  01988559  01988631  01989393   01989933   01990047
        01990311   01992522   01993866   01994088  01994108   01994373  01994483   01995018  01995109  01995589   01995656   01997414  01997759
        01997826   01997940   01998370   01998451   01998999  01999351   H344502   H409129   H460562   H794171
 [ reached getOption("max.print") -- omitted 274 rows ]
attr(,"names")
   [1] "01001621" "01001661" "01002192" "01002471" "01002885" "01003555" "01003757" "01003758" "01004037" "01004350" "01004551" "01004575" "01004596"
  [14] "01005890" "01005935" "01006225" "01007074" "01007520" "01008097" "01008265" "01008612" "01008631" "01009390" "01009735" "01010189" "01010305"
  [27] "01010353" "01010869" "01012428" "01012516" "01012545" "01013541" "01013762" "01014138" "01014816" "01015440" "01016929" "01017180" "01017741"
  [40] "01017775" "01018937" "01019058" "01019071" "01020175" "01020229" "01021265" "01022339" "01022402" "01022407" "01022858" "01023349" "01023413"
  [53] "01024256" "01024456" "01024630" "01025234" "01025263" "01026607" "01027404" "01027470" "01027537" "01027696" "01029036" "01029221" "01030956"
  [66] "01031512" "01031642" "01032098" "01032815" "01033859" "01034215" "01034290" "01034766" "01035670" "01035805" "01036226" "01037387" "01037980"
  [79] "01038044" "01039187" "01039289" "01039498" "01040030" "01040480" "01041616" "01041867" "01042050" "01043932" "01044249" "01044589" "01046903"
  [92] "01047354" "01047811" "01047962" "01048336" "01048590" "01049187" "01049423" "01049882" "01050730" "01051449" "01051853" "01052106" "01052129"
 [105] "01052191" "01052215" "01053273" "01053715" "01054467" "01054605" "01054953" "01055203" "01056512" "01056539" "01057091" "01057221" "01057494"
 [118] "01057853" "01058129" "01058286" "01058434" "01058551" "01059446" "01061685" "01062180" "01062548" "01062832" "01063155" "01063600" "01063898"
 [131] "01064166" "01064217" "01065495" "01065558" "01066219" "01066842" "01068049" "01068336" "01068343" "01068688" "01069664" "01070356" "01070706"
 [144] "01073086" "01074064" "01074358" "01074839" "01074895" "01074919" "01075262" "01075315" "01075653" "01075936" "01076617" "01076849" "01077614"
 [157] "01078359" "01080714" "01081740" "01081870" "01082166" "01082219" "01083356" "01084760" "01084848" "01084936" "01084981" "01086028" "01086260"
 [170] "01087775" "01089152" "01089730" "01090699" "01090966" "01090985" "01091344" "01092042" "01092578" "01093067" "01095263" "01095550" "01095708"
 [183] "01096598" "01097616" "01098111" "01098582" "01098662" "01098708" "01098954" "01099022" "01099707" "01099807" "01100090" "01100093" "01100301"
 [196] "01100470" "01100787" "01101113" "01101580" "01101818" "01103169" "01104524" "01104998" "01105349" "01106206" "01106734" "01107318" "01107786"
 [209] "01108839" "01109152" "01109311" "01110511" "01110652" "01111390" "01112052" "01112213" "01113056" "01114773" "01115185" "01116419" "01116698"
 [222] "01117506" "01117666" "01118407" "01118887" "01119219" "01119278" "01119799" "01121094" "01121281" "01121506" "01124068" "01124554" "01124557"
 [235] "01124640" "01125510" "01125762" "01125820" "01126088" "01126228" "01126385" "01126611" "01127076" "01127950" "01128019" "01128104" "01128604"
 [248] "01129252" "01129287" "01129614" "01130475" "01131525" "01131755" "01131862" "01133020" "01133190" "01133488" "01133979" "01134467" "01134627"
 [261] "01134852" "01134952" "01135208" "01135229" "01135434" "01135564" "01137439" "01137764" "01138494" "01139462" "01139724" "01139766" "01140117"
 [274] "01140554" "01140660" "01141322" "01141335" "01141375" "01141632" "01142145" "01142225" "01142570" "01142696" "01143086" "01144986" "01145585"
 [287] "01146647" "01147280" "01147647" "01147722" "01147944" "01148342" "01148826" "01149234" "01149874" "01149887" "01150088" "01150133" "01150256"
 [300] "01150534" "01150670" "01151826" "01152251" "01153383" "01154032" "01154257" "01154691" "01154821" "01154856" "01154995" "01155523" "01155774"
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 [352] "01175411" "01175547" "01176588" "01177180" "01178071" "01178677" "01179608" "01179898" "01179920" "01180272" "01180825" "01181433" "01181682"
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 [443] "01217376" "01217679" "01218073" "01218722" "01219514" "01220660" "01220747" "01221266" "01221452" "01221476" "01221559" "01221615" "01221688"
 [456] "01222182" "01222286" "01222611" "01223735" "01223957" "01224453" "01224526" "01224606" "01225255" "01226325" "01226780" "01226903" "01227044"
 [469] "01227858" "01228251" "01228476" "01230087" "01230333" "01230573" "01230961" "01231502" "01231615" "01231638" "01231872" "01232606" "01232614"
 [482] "01233310" "01233390" "01233782" "01234668" "01236052" "01236168" "01236520" "01236818" "01238327" "01238340" "01238425" "01238758" "01239027"
 [495] "01239160" "01239686" "01240429" "01240762" "01240947" "01241681" "01241841" "01242341" "01242582" "01243433" "01243762" "01244248" "01244421"
 [508] "01245094" "01247637" "01248541" "01248818" "01248894" "01249756" "01250217" "01250241" "01250454" "01251537" "01252056" "01252144" "01252784"
 [521] "01253011" "01256739" "01256840" "01257335" "01258294" "01258737" "01259522" "01259658" "01259738" "01259834" "01261412" "01261654" "01262152"
 [534] "01262983" "01263023" "01263493" "01264795" "01265498" "01265757" "01266086" "01267255" "01267393" "01267420" "01268471" "01268568" "01268746"
 [547] "01268785" "01270078" "01270317" "01270608" "01271411" "01272145" "01272380" "01273646" "01274049" "01274423" "01274979" "01275666" "01275996"
 [560] "01277016" "01277584" "01277761" "01277837" "01277838" "01278177" "01278714" "01279158" "01279889" "01280219" "01280637" "01282093" "01282698"
 [573] "01283684" "01283804" "01284672" "01285204" "01285575" "01285674" "01285871" "01286296" "01286616" "01286934" "01286951" "01287002" "01287040"
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 [625] "01309498" "01309779" "01310126" "01310175" "01310261" "01310389" "01311144" "01311318" "01311478" "01311579" "01311895" "01312483" "01312666"
 [638] "01312792" "01313082" "01313668" "01313904" "01314140" "01314788" "01315038" "01315327" "01315770" "01316569" "01317285" "01317702" "01317813"
 [651] "01318417" "01319136" "01319649" "01319761" "01320932" "01321418" "01321719" "01321726" "01321820" "01321856" "01322694" "01323045" "01323399"
 [664] "01323640" "01323952" "01324141" "01324463" "01325310" "01325488" "01327998" "01328835" "01329753" "01330626" "01330925" "01331422" "01331474"
 [677] "01331668" "01331916" "01331919" "01333624" "01333878" "01334323" "01334416" "01334918" "01334958" "01335458" "01335521" "01336320" "01336644"
 [690] "01336769" "01337343" "01337814" "01337835" "01338370" "01338813" "01340139" "01341810" "01342346" "01342354" "01344468" "01345091" "01345295"
 [703] "01345440" "01346074" "01346645" "01349077" "01349596" "01349735" "01349868" "01350228" "01351892" "01352867" "01353189" "01353572" "01354461"
 [716] "01354787" "01354879" "01354930" "01355010" "01355342" "01355514" "01355617" "01355791" "01356466" "01357653" "01357953" "01358815" "01358940"
 [729] "01359817" "01359989" "01360278" "01360318" "01360677" "01361259" "01361414" "01361790" "01362364" "01362687" "01363795" "01364126" "01364192"
 [742] "01364721" "01365052" "01365832" "01366208" "01366706" "01366783" "01367738" "01367896" "01367974" "01368088" "01368213" "01368780" "01369634"
 [755] "01370236" "01370608" "01371698" "01371934" "01372136" "01372590" "01373046" "01374010" "01374127" "01374976" "01375514" "01376324" "01377852"
 [768] "01378031" "01378108" "01378660" "01378885" "01380424" "01381144" "01381548" "01382653" "01382823" "01384009" "01384259" "01384536" "01385380"
 [781] "01386387" "01386550" "01386776" "01386876" "01387787" "01388193" "01388232" "01388366" "01389526" "01390496" "01390540" "01391396" "01392716"
 [794] "01392841" "01393107" "01393734" "01394181" "01394556" "01395120" "01395563" "01395662" "01395750" "01395906" "01396584" "01396601" "01397710"
 [807] "01397998" "01398191" "01398959" "01399040" "01399383" "01399987" "01400682" "01401259" "01401611" "01401924" "01402249" "01403452" "01404254"
 [820] "01404344" "01404357" "01405352" "01405518" "01405558" "01405967" "01407492" "01409276" "01409471" "01410286" "01411311" "01411551" "01411874"
 [833] "01411965" "01412088" "01412110" "01412199" "01414715" "01414861" "01415267" "01416835" "01417740" "01419702" "01419731" "01420162" "01421501"
 [846] "01422755" "01423895" "01425137" "01425460" "01426287" "01426848" "01427224" "01427331" "01428031" "01428273" "01429795" "01431847" "01432148"
 [859] "01433659" "01433801" "01434051" "01434290" "01434465" "01434970" "01435210" "01436011" "01436147" "01436826" "01436873" "01438254" "01440274"
 [872] "01440344" "01440882" "01441047" "01441388" "01442539" "01443291" "01443715" "01443984" "01444005" "01445121" "01445177" "01445418" "01446014"
 [885] "01446768" "01446781" "01446975" "01447446" "01448060" "01448663" "01449363" "01449403" "01449816" "01450565" "01451049" "01451795" "01452282"
 [898] "01453251" "01453593" "01453805" "01454929" "01455153" "01455437" "01456065" "01456773" "01457096" "01457354" "01457675" "01457928" "01458377"
 [911] "01458643" "01459084" "01459367" "01460209" "01460589" "01460874" "01461000" "01462010" "01463203" "01463442" "01463928" "01464386" "01465504"
 [924] "01465880" "01466144" "01466897" "01467082" "01467301" "01468509" "01469251" "01469484" "01471106" "01471283" "01471958" "01472130" "01472282"
 [937] "01473368" "01474611" "01474832" "01475942" "01476994" "01477610" "01478283" "01478460" "01478952" "01479699" "01480898" "01481827" "01481963"
 [950] "01482624" "01482696" "01483006" "01483595" "01483880" "01484509" "01484820" "01484900" "01485116" "01486207" "01486356" "01487038" "01487379"
 [963] "01487429" "01487447" "01487936" "01489017" "01489961" "01491050" "01491245" "01491438" "01492075" "01492335" "01492554" "01493928" "01494796"
 [976] "01495022" "01496084" "01496511" "01497137" "01497187" "01497207" "01497564" "01498008" "01498133" "01498419" "01498803" "01498819" "01499133"
 [989] "01500041" "01500706" "01500905" "01501052" "01501394" "01502061" "01502166" "01502372" "01502665" "01502955" "01503152" "01503991"
 [ reached getOption("max.print") -- omitted 546726 entries ]

countdf

meta

anno

full

# Design linear regression models using lmFit and eBayes with LIMMA
# This code adapted from Christian Diener, PhD:
design <- model.matrix(~bowel + sex + age + BMI_CALC + eGFR, meta) # Covariates: sex, age, BMI
#dge <- DGEList(counts=count_matrix)  # Where `count_matrix` is the matrix mentioned above
#dge <- calcNormFactors(count_matrix)  # Normalize the matrix (this step only for CORNCOB/microbiome data)
#logCPM <- cpm(dge, log=FALSE)  # Takes the log of the data
fit <- lmFit(count_matrix, design)  # Fits the model for all metabolites
fit <- eBayes(fit)  # Stabilizes the variances
fit

#Pre-process for plotting:
#Get results table for Constipation coefficient relative to High Normal BMF:
re_const <- topTable(fit, coef = 2, genelist = anno, sort="p", number="none")  # Select the significant models by coefficient 2

indices <- match(rownames(re_const), rownames(anno)) # associate column of protein names with index of protein
re_const[1] <- anno$name[indices] # associate the protein names with the protein indices
re_const <- dplyr::inner_join(anno, re_const, by = intersect(names(anno),names(re_const))) #combine anno and re dfs by intersection of protein values
p_const <- re_const[re_const$adj.P.Val < 0.05,] # create df of just significant adj P value results
p_const <- p_const[order(p_const$adj.P.Val),] # order by adj P value
p_const <- p_const[,c('logFC','B','adj.P.Val','name','panel','uniprot','gene_name','gene_description','P.Value')] # keep only desired columns

#Pre-process for plotting:
#Get results table for Low Normal coefficient relative to High Normal BMF:
re_low <- topTable(fit, coef = 3, genelist = anno, sort="p", number="none")  # Select the significant models by coefficient 3

indices <- match(rownames(re_low), rownames(anno)) # associate column of protein names with index of protein
re_low[1] <- anno$name[indices] # associate the protein names with the protein indices
re_low <- dplyr::inner_join(anno, re_low, by = intersect(names(anno),names(re_low))) #combine anno and re dfs by intersection of protein values
p_low <- re_low[re_low$adj.P.Val < 0.05,] # create df of just significant adj P value results
p_low <- p_low[order(p_low$adj.P.Val),] # order by adj P value
p_low <- p_low[,c('logFC','B','adj.P.Val','name','panel','uniprot','gene_name','gene_description','P.Value')] # keep only desired columns

#Pre-process for plotting:
#Get results table for Diarrhea coefficient relative to High Normal BMF:
re_diarrhea <- topTable(fit, coef = 4, genelist = anno, sort="p", number="none")  # Select the significant models by coefficient 4

indices <- match(rownames(re_diarrhea), rownames(anno)) # associate column of protein names with index of protein
re_diarrhea[1] <- anno$name[indices] # associate the protein names with the protein indices
re_diarrhea <- dplyr::inner_join(anno, re_diarrhea, by = intersect(names(anno),names(re_diarrhea))) #combine anno and re dfs by intersection of protein values
p_diarrhea <- re_diarrhea[re_diarrhea$adj.P.Val < 0.05,] # create df of just significant adj P value results
p_diarrhea <- p_diarrhea[order(p_diarrhea$adj.P.Val),] # order by adj P value
p_diarrhea <- p_diarrhea[,c('logFC','B','adj.P.Val','name','panel','uniprot','gene_name','gene_description','P.Value')] # keep only desired columns



#Show dfs of significant hits (there are none)
sig_const <- p_const[which(p_const$adj.P.Val < 0.05),]
sig_low <- p_low[which(p_low$adj.P.Val < 0.05),]
sig_diarrhea <- p_diarrhea[which(p_diarrhea$adj.P.Val < 0.05),]
rbind(sig_const,sig_low,sig_diarrhea)

p_df <- re_const

# Significant Genera Plot (plot 0)
plot0c <- ggplot(p_df, mapping = aes(x=logFC,y = -log10(P.Value), color = ifelse(adj.P.Val < 0.05,gene_name,"- Adj FDR P > 0.05")))+
  geom_point(size=0.1) +
  #geom_text(size=2.5, aes(label = ifelse(Severe.P.Val < 0.05,Combined,""), color = ifelse(Severe.P.Val < 0.05,Combined,"")),hjust = -0.05, vjust = 1, angle = 30, position = position_jitter(seed = 1))+
  geom_jitter(position = position_jitter(seed = 1)) +
 #ggtitle("Significant Genera") +
  xlab(bquote(beta~" Coefficient")) + 
  ylab(bquote("-log"[10]~"(P value)")) + 
  geom_vline(xintercept = 0)+
  #scale_x_break(c(-0.6, -18)) +
  scale_x_continuous(name = bquote(atop(beta["BMF"]~" Coefficient",italic("Constipation"))), 
                     guide = guide_axis(n.dodge = 2)) +
  theme(text = 
          element_text(size = 14), 
        plot.title = element_text(vjust = 0.5), 
        #plot.subtitle = element_text(size=8, hjust = 0.5), 
        legend.title = element_blank(), 
        axis.title.y = element_text(size = 8),
        axis.title.x = element_text(size = 8),
              legend.text  = element_text(size = 6.5),
              legend.position = "top",
              legend.box.just = "top",
              legend.key.size = unit(0.01, "cm"),
              legend.box.margin = margin(0, 1, 0, 1))+
          guides(shape = guide_legend(override.aes = list(size = 1)),
               color = guide_legend(override.aes = list(size = 1), nrow = 30),
               fill=guide_legend(title=NULL))
plot0c

p_df <- re_low

# Significant Genera Plot (plot 0)
plot0l <- ggplot(p_df, mapping = aes(x=logFC,y = -log10(P.Value), color = ifelse(adj.P.Val < 0.05,gene_name,"- Adj FDR P > 0.05")))+
  geom_point(size=0.1) +
  #geom_text(size=2.5, aes(label = ifelse(Severe.P.Val < 0.05,Combined,""), color = ifelse(Severe.P.Val < 0.05,Combined,"")),hjust = -0.05, vjust = 1, angle = 30, position = position_jitter(seed = 1))+
  geom_jitter(position = position_jitter(seed = 1)) +
 #ggtitle("Significant Genera") +
  xlab(bquote(beta~" Coefficient")) + 
  ylab(bquote("-log"[10]~"(P value)")) + 
  geom_vline(xintercept = 0)+
  #scale_x_break(c(-0.6, -18)) +
  scale_x_continuous(name = bquote(atop(beta["BMF"]~" Coefficient",italic("Low Normal"))), 
                     guide = guide_axis(n.dodge = 2)) +
  theme(text = 
          element_text(size = 14), 
        plot.title = element_text(vjust = 0.5), 
        #plot.subtitle = element_text(size=8, hjust = 0.5), 
        legend.title = element_blank(), 
        axis.title.y = element_text(size = 8),
        axis.title.x = element_text(size = 8),
              legend.text  = element_text(size = 6.5),
              legend.position = "top",
              legend.box.just = "top",
              legend.box.margin = margin(0, 1, 0, 1),
              legend.key.size = unit(0.01,"cm"))+
          guides(shape = guide_legend(override.aes = list(size = 1)),
               color = guide_legend(override.aes = list(size = 1), nrow = 30),
               fill=guide_legend(title=NULL))
plot0l

p_df <- re_diarrhea

# Significant Genera Plot (plot 0)
plot0d <- ggplot(p_df, mapping = aes(x=logFC,y = -log10(P.Value), color = ifelse(adj.P.Val < 0.05,gene_name,"- Adj FDR P > 0.05")))+
  geom_point(size=0.1) +
  #geom_text(size=2.5, aes(label = ifelse(Severe.P.Val < 0.05,Combined,""), color = ifelse(Severe.P.Val < 0.05,Combined,"")),hjust = -0.05, vjust = 1, angle = 30, position = position_jitter(seed = 1))+
  geom_jitter(position = position_jitter(seed = 1)) +
 #ggtitle("Significant Genera") +
  xlab(bquote(beta~" Coefficient")) + 
  ylab(bquote("-log"[10]~"(P value)")) + 
  geom_vline(xintercept = 0)+
  #scale_x_break(c(-0.6, -18)) +
  scale_x_continuous(name = bquote(atop(beta["BMF"]~" Coefficient",italic("Diarrhea"))), 
                     guide = guide_axis(n.dodge = 2)) +
  theme(text = 
          element_text(size = 14), 
        plot.title = element_text(vjust = 0.5), 
        #plot.subtitle = element_text(size=8, hjust = 0.5), 
        legend.title = element_blank(), 
        axis.title.y = element_text(size = 8),
        axis.title.x = element_text(size = 8),
              legend.text  = element_text(size = 6.5),
              legend.position = "top",
              legend.box.just = "top",
              legend.box.margin = margin(0, 1, 0, 1),
              legend.key.size = unit(0.01, "cm"))+
          guides(shape = guide_legend(override.aes = list(size = 1)),
               color = guide_legend(override.aes = list(size = 1), nrow = 30),
               fill=guide_legend(title=NULL))
plot0d

library(ggpubr)
figure <- ggarrange(plot0c,plot0l,plot0d, common.legend = FALSE, nrows = 1, ncols = 3, align = "hv", legend = "bottom", widths = c(1,1,1), heights = c(50,50,50)) + theme(plot.margin = unit(c(0,5,0,5), "cm"),        #plot.subtitle = element_text(size=8, hjust = 0.5), 
        legend.title = element_blank(), 
        axis.title.y = element_text(size = 8),
        axis.title.x = element_text(size = 8),
              legend.text  = element_text(size = 8),
              legend.position = "bottom",
              legend.box.just = "bottom",
              legend.box.margin = margin(0, 1, 0, 1),
              legend.key.size = unit(0.01,"cm"))+
          guides(shape = guide_legend(override.aes = list(size = 1)),
               color = guide_legend(override.aes = list(size = 1), nrow = 20),
               fill=guide_legend(title=NULL))

ggsave(
  "BMFvsProteinsVolcano.png",
  plot = figure,
  device = NULL,
  path = NULL,
  scale = 2.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 18.2 x 11.3 in image

figure

#Plotting
library(tidyverse)
library(dplyr)
library(ggpubr)
library(stringr)
library(ggprism)
library(patchwork)
library(magrittr)
library(ggbeeswarm)
library(data.table)
comparisons = list(c("Low Normal","High Normal"),c("Diarrhea","High Normal"))

sig_low['bowel'] <- rep('Low Normal',1)
sig_diarrhea['bowel']  <- rep('Diarrhea',25)
bound <- rbind(sig_low,sig_diarrhea)
a <- full[,names(full) %in% bound['name'][[1]]]
setcolorder(a,bound['name'][[1]])
names(a) <- bound['gene_name'][[1]]
biochemistry <- cbind(full[,1:6],a)
names(biochemistry)[11] <- 'TNFRSF11B (2nd Instance)'
biochemistry$bowel <- factor(biochemistry$bowel, levels=c(1,2,3,4), labels = c("Constipation","Low Normal","High Normal","Diarrhea"))
biochemistry$bowel <- factor(biochemistry$bowel, levels=c("Constipation","Low Normal","High Normal","Diarrhea"), labels = c("Constipation","Low Normal","High Normal","Diarrhea"))


sig = function(x){
  if(x < 0.001){"***"} 
  else if(x < 0.01){"**"}
  else if(x < 0.05){"*"}
  else{NA}}

test = function(x,y,z,j) {
  x = NULL
  y = NULL
  if (counter == 1) {
    z = comparisons[[1]][1]
  } else {
    z = comparisons[[2]][1]
    counter<<-0
  }
  print(j)
  if (str_detect(z,"Diarrhea") & any(sig_diarrhea[,'gene_name']==j)) {
    results = list(p.value = sig_diarrhea[which(sig_diarrhea[,'gene_name']==j),]['adj.P.Val'][[1]])
  } else if (str_detect(z,"Low Normal") & any(sig_low[,'gene_name']==j)) {
    results = list(p.value = sig_low[which(sig_low[,'gene_name']==j),]['adj.P.Val'][[1]])
  } else {
    results = list(p.value = 1)
  }
  names(results) <- 'p.value'
  counter <<-counter+1
  return(results)
}

counter <<-1
myplots <- list()  # new empty list
  for (ind in 1:(ncol(biochemistry)-6)) {
  myplots[[ind]] <- local({
    label = paste(names(biochemistry)[ind+6],sep="")
    sub = ifelse(label!='TNFRSF11B (2nd Instance)',paste(bound['gene_description'][which(bound['gene_name']==label),]),paste(bound['gene_description'][which(bound['gene_name']=='TNFRSF11B'),]))
    print(label)
    print(sub)
    n = 2
    plotlim_lower = min(biochemistry[!is.na(biochemistry[,ind+6]),][,ind+6])
    plotlim_upper = max(biochemistry[!is.na(biochemistry[,ind+6]),][,ind+6])
    plotlim_bar = plotlim_lower - n
    plotlim_margin = abs(plotlim_bar - n*10)
    sublabel <- 'TNFRSF11B'
    plt <- ggplot(data = biochemistry, aes(x = bowel, y = .data[[label]], group = bowel)) +
    scale_x_discrete(guide = guide_axis(n.dodge = 2))+
    geom_beeswarm(aes(color = bowel), size = 0.1, cex = 0.5) +
    geom_boxplot(alpha=0.0,outlier.shape = NA) +
    theme(text = element_text(size = 9)) +
    ggtitle(label = str_wrap(label, width = 2),subtitle=str_wrap(sub,width=20)) +
    coord_cartesian(ylim=c(plotlim_lower,plotlim_upper),clip="off")+
    geom_signif(comparisons = comparisons, map_signif_level = sig, test = 'test', test.args = list(z = comparisons, j = ifelse(label!='TNFRSF11B (2nd Instance)',label,sublabel)), 
                y_position = plotlim_bar, 
                step_increase = 0.10,  size = 0.5, 
                textsize = 1.5,
                tip_length = c(0,0)) +
    labs(color = "BMF Category", y = ifelse((ind == 1 | ind == 6 | ind == 11 | ind == 16 | ind == 21 | ind == 26),"Protein Level",""))+
    guides(colour = guide_legend(override.aes = list(size=7), title.position = 'left', nrow = 1, ncol = 4)) +
    theme(plot.margin = unit(c(0,0,plotlim_margin,0), "pt"),  
          plot.title = element_text(size=5.75), 
          plot.subtitle = element_text(size=4.5), 
          legend.title = element_text(size=10),
          legend.text = element_text(size=7),
          axis.text.x = element_blank(), 
          axis.text.y = element_text(size=7), 
          axis.title.y = element_text(size=7),
          axis.title.x = element_blank(),
          aspect.ratio = 0.95)+
    scale_fill_manual(limits = c("Constipation","Low Normal","High Normal","Diarrhea"), labels = c("Constipation","Low Normal","High Normal","Diarrhea"), values = colors(),
                    drop = FALSE)
  print(plt)
  })
}

[1] "HAVCR1"
[1] "hepatitis A virus cellular receptor 1"
[1] "HAVCR1"
[1] "HAVCR1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B"
[1] "tumor necrosis factor receptor superfamily, member 11b"
[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "HGF"
[1] "hepatocyte growth factor (hepapoietin A; scatter factor)"
[1] "HGF"
[1] "HGF"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TEK"
[1] "TEK tyrosine kinase, endothelial"
[1] "TEK"
[1] "TEK"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B (2nd Instance)"
[1] "tumor necrosis factor receptor superfamily, member 11b"
[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TGFA"
[1] "transforming growth factor, alpha"
[1] "TGFA"
[1] "TGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CSF1"
[1] "colony stimulating factor 1 (macrophage)"
[1] "CSF1"
[1] "CSF1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "VEGFA"
[1] "vascular endothelial growth factor A"
[1] "VEGFA"
[1] "VEGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LIFR"
[1] "leukemia inhibitory factor receptor alpha"
[1] "LIFR"
[1] "LIFR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF9"
[1] "tumor necrosis factor receptor superfamily, member 9"
[1] "TNFRSF9"
[1] "TNFRSF9"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "IL10RB"
[1] "interleukin 10 receptor, beta"
[1] "IL10RB"
[1] "IL10RB"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "FST"
[1] "follistatin"
[1] "FST"
[1] "FST"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "GDF15"
[1] "growth differentiation factor 15"
[1] "GDF15"
[1] "GDF15"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "RARRES2"
[1] "retinoic acid receptor responder (tazarotene induced) 2"
[1] "RARRES2"
[1] "RARRES2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "IL1RN"
[1] "interleukin 1 receptor antagonist"
[1] "IL1RN"
[1] "IL1RN"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LDLR"
[1] "low density lipoprotein receptor"
[1] "LDLR"
[1] "LDLR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSD"
[1] "cathepsin D"
[1] "CTSD"
[1] "CTSD"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ICAM2"
[1] "intercellular adhesion molecule 2"
[1] "ICAM2"
[1] "ICAM2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ADA"
[1] "adenosine deaminase"
[1] "ADA"
[1] "ADA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "PLAUR"
[1] "plasminogen activator, urokinase receptor"
[1] "PLAUR"
[1] "PLAUR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TREML2"
[1] "triggering receptor expressed on myeloid cells-like 2"
[1] "TREML2"
[1] "TREML2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSZ"
[1] "cathepsin Z"
[1] "CTSZ"
[1] "CTSZ"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL4"
[1] "chemokine (C-C motif) ligand 4"
[1] "CCL4"
[1] "CCL4"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "DNER"
[1] "delta/notch-like EGF repeat containing"
[1] "DNER"
[1] "DNER"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL13"
[1] "chemokine (C-C motif) ligand 13"
[1] "CCL13"
[1] "CCL13"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CDCP1"
[1] "CUB domain containing protein 1"
[1] "CDCP1"
[1] "CDCP1"

Warning: Removed 3 rows containing missing values (geom_signif).

library(r2symbols)
counter<<- 1
figure1 <- ggarrange(plotlist = myplots[1:10], labels = c(LETTERS[1:10]), legend = "top", align = "hv", font.label = list(size = 9.5), common.legend = TRUE, nrow = 2, ncol = 5, legend.grob = get_legend(myplots[[2]]))

[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "HAVCR1"
[1] "HAVCR1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "HGF"
[1] "HGF"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TEK"
[1] "TEK"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF11B"
[1] "TNFRSF11B"

Warning in if (x < 0.001) { :
  the condition has length > 1 and only the first element will be used
Warning in if (x < 0.01) { :
  the condition has length > 1 and only the first element will be used
Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TGFA"
[1] "TGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CSF1"
[1] "CSF1"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "VEGFA"
[1] "VEGFA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LIFR"
[1] "LIFR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "TNFRSF9"
[1] "TNFRSF9"

Warning: Removed 3 rows containing missing values (geom_signif).

                                                                                                             
figure1

counter <<- 1
figure2 <- ggarrange(plotlist = myplots[11:20], labels = c(LETTERS[11:20]), font.label = list(size = 9.5), legend = "none", align = "hv", nrow = 2, ncol = 5)

[1] "IL10RB"
[1] "IL10RB"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "FST"
[1] "FST"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "GDF15"
[1] "GDF15"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "RARRES2"
[1] "RARRES2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "IL1RN"
[1] "IL1RN"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "LDLR"
[1] "LDLR"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSD"
[1] "CTSD"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ICAM2"
[1] "ICAM2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "ADA"
[1] "ADA"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "PLAUR"
[1] "PLAUR"

Warning: Removed 3 rows containing missing values (geom_signif).

figure2

counter <<- 1
figure3 <- ggarrange(plotlist = myplots[21:26], labels = c(LETTERS[21:26]), font.label = list(size = 9.5), legend = "none", align = "hv", nrow = 2, ncol = 5)

[1] "TREML2"
[1] "TREML2"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CTSZ"
[1] "CTSZ"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL4"
[1] "CCL4"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "DNER"
[1] "DNER"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CCL13"
[1] "CCL13"

Warning: Removed 3 rows containing missing values (geom_signif).

[1] "CDCP1"
[1] "CDCP1"

Warning: Removed 3 rows containing missing values (geom_signif).

figure3

#Main Text Figures
counter <<- 1
figure_main1 <- ggarrange(plotlist = list(figure1,figure2,figure3), legend = "top", align = "hv", common.legend = TRUE, nrow = 3, ncol = 1)
figure_main1

counter <<- 1
ggsave(
  "BMFvsProteins1.png",
  plot = figure1,
  device = NULL,
  path = NULL,
  scale = 1.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

counter <<- 1
ggsave(
  "BMFvsProteins2.png",
  plot = figure2,
  device = NULL,
  path = NULL,
  scale = 1.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

counter <<- 1
ggsave(
  "BMFvsProteins3.png",
  plot = figure3,
  device = NULL,
  path = NULL,
  scale = 1.5,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

counter <<- 1
ggsave(
  "BMFvsProteinsMain.png",
  plot = figure_main1,
  device = NULL,
  path = NULL,
  scale = 100,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL
)

Saving 7 x 7 in image

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.